Learning Kinetic Distance Metrics for Markov State Models of Protein Conformational Dynamics

2013 ◽  
Vol 9 (7) ◽  
pp. 2900-2906 ◽  
Author(s):  
Robert T. McGibbon ◽  
Vijay S. Pande
Keyword(s):  
Conformational Dynamics ◽  
Distance Metrics ◽  
Markov State ◽  
Markov State Models ◽  
Protein Conformational Dynamics ◽  
State Models

scholarly journals Multiscale Conformational Dynamics of Trp-Cage by Markov State Models

2012 ◽  
Vol 102 (3) ◽  
pp. 170a
Author(s):  
Camilo A. Jimenez-Cruz ◽  
Angel E. Garcia
Keyword(s):  
Conformational Dynamics ◽  
Markov State ◽  
Markov State Models ◽  
State Models

scholarly journals Reconciling simulated ensembles of apomyoglobin with experimental HDX data using Bayesian inference and multi-ensemble Markov State Models

2019 ◽  
Author(s):  
Hongbin Wan ◽  
Yunhui Ge ◽  
Asghar Razavi ◽  
Vincent A. Voelz
Keyword(s):  
Molecular Dynamics ◽  
Bayesian Inference ◽  
Conformational Dynamics ◽  
Molecular Simulations ◽  
Dynamics Simulation ◽  
Trajectory Data ◽  
Protection Factor ◽  
Markov State ◽  
Markov State Models ◽  
State Models

AbstractHydrogen/deuterium exchange (HDX) is a powerful technique to investigate protein conformational dynamics at amino acid resolution. Because HDX provides a measurement of solvent exposure of backbone hydrogens, ensemble-averaged over potentially slow kinetic processes, it has been challenging to use HDX protection factors to refine structural ensembles obtained from molecular dynamics simulations. This entails two dual challenges: (1) identifying structural observables that best correlate with backbone amide protection from exchange, and (2) restraining these observables in molecular simulations to model ensembles consistent with experimental measurements. Here, we make significant progress on both fronts. First, we describe an improved predictor of HDX protection factors from structural observables in simulated ensembles, parameterized from ultra-long molecular dynamics simulation trajectory data, with a Bayesian inference approach used to retain the full posterior distribution of model parameters.We next present a new method for obtaining simulated ensembles in agreement with experimental HDX protection factors, in which molecular simulations are performed at various temperatures and restraint biases, and used to construct multi-ensemble Markov State Models (MSMs). Finally, the BICePs algorithm (Bayesian Inference of Conformational Populations) is then used with our HDX protection factor predictor to infer which thermodynamic ensemble agrees best with experiment, and estimate populations of each conformational state in the MSM. To illustrate the approach, we use a combination of HDX protection factor restraints and chemical shift restraints to model the conformational ensemble of apomyoglobin at pH 6. The resulting ensemble agrees well with experiment, and gives insight into the all-atom structure of disordered helices F and H in the absence of heme.Graphical TOC Entry

scholarly journals Building Markov State Models Using Optimal Transport Theory

2018 ◽  
Author(s):  
Purushottam Dixit ◽  
Ken Dill
Keyword(s):  
Transport Theory ◽  
Optimal Transport ◽  
Conformational Dynamics ◽  
Computational Estimation ◽  
Markov State ◽  
Markov State Models ◽  
Path Cost ◽  
Transient Events ◽  
State Models ◽  
Optimal Transport Theory

Markov State Models (MSMs) describe the rates and routes in conformational dynamics of biomolecules. Computational estimation of MSMs can be expensive because<br>molecular simulations are slow to nd and sample the rare transient events. We describe here an e cient approximate way to determine MSM rate matrices by combining Maximum Caliber (maximizing path entropies) with Optimal Transport Theory (minimizing some path cost function, as when routing trucks on transportation<br>networks) to patch together transient dynamical information from multiple nonequilibrium<br>simulations. We give toy examples.

scholarly journals Building Markov State Models Using Optimal Transport Theory

2018 ◽  
Author(s):  
Purushottam Dixit ◽  
Ken Dill
Keyword(s):  
Transport Theory ◽  
Optimal Transport ◽  
Conformational Dynamics ◽  
Computational Estimation ◽  
Markov State ◽  
Markov State Models ◽  
Path Cost ◽  
Transient Events ◽  
State Models ◽  
Optimal Transport Theory

Markov State Models (MSMs) describe the rates and routes in conformational dynamics of biomolecules. Computational estimation of MSMs can be expensive because<br>molecular simulations are slow to nd and sample the rare transient events. We describe here an e cient approximate way to determine MSM rate matrices by combining Maximum Caliber (maximizing path entropies) with Optimal Transport Theory (minimizing some path cost function, as when routing trucks on transportation<br>networks) to patch together transient dynamical information from multiple nonequilibrium<br>simulations. We give toy examples.

scholarly journals Markov State Models and NMR Uncover an Overlooked Allosteric Loop in p53

2020 ◽  
Author(s):  
Emilia P. Barros ◽  
Özlem Demir ◽  
Jenaro Soto ◽  
Melanie J. Cocco ◽  
Rommie E. Amaro
Keyword(s):  
Protein Function ◽  
Human Cancer ◽  
Conformational Dynamics ◽  
Nmr Relaxation ◽  
Markov State ◽  
Markov State Models ◽  
Distant Location ◽  
Protein Ensembles ◽  
State Models ◽  
Dynamics Simulations

ABSTRACTThe tumor suppressor p53 is the most frequently mutated gene in human cancer, and thus reactivation of mutated p53 is a promising avenue for cancer therapy. Analysis of wildtype p53 and the Y220C cancer mutant long-timescale molecular dynamics simulations with Markov state models and validation by NMR relaxation studies has uncovered the involvement of loop L6 in the slowest motions of the protein. Due to its distant location from the DNA-binding surface, the conformational dynamics of this loop has so far remained largely unexplored. We observe mutation-induced stabilization of alternate L6 conformations, distinct from all experimentally-determined structures, in which the loop is both extended and located further away from the DNA-interacting surface. Additionally, the effect of the L6-adjacent Y220C mutation on the conformational landscape of the functionally-important loop L1 suggests an allosteric role to this dynamic loop and the inactivation mechanism of the mutation. Finally, the simulations reveal a novel Y220C cryptic pocket that can be targeted for p53 rescue efforts. Our approach exemplifies the power of the MSM methodology for uncovering intrinsic dynamic and kinetic differences among distinct protein ensembles, such as for the investigation of mutation effects on protein function.

Elucidation of the conformational dynamics of multi-body systems by construction of Markov state models

2016 ◽  
Vol 18 (44) ◽  
pp. 30228-30235 ◽  
Author(s):  
Lizhe Zhu ◽  
Fu Kit Sheong ◽  
Xiangze Zeng ◽  
Xuhui Huang
Keyword(s):  
Conformational Dynamics ◽  
Efficient Estimation ◽  
Model Construction ◽  
State Model ◽  
Optimal State ◽  
Markov State Model ◽  
Markov State ◽  
Markov State Models ◽  
State Models ◽  
Multi Body

Recent algorithmic progresses in Markov State Model construction that enable optimal state definition and efficient estimation of the slow uphill kinetics are expected to boost investigations of complex multi-body processes.

Surprisal Metrics for Quantifying Perturbed Conformational Dynamics in Markov State Models

2014 ◽  
Vol 10 (12) ◽  
pp. 5716-5728 ◽  
Author(s):  
Vincent A. Voelz ◽  
Brandon Elman ◽  
Asghar M. Razavi ◽  
Guangfeng Zhou
Keyword(s):  
Conformational Dynamics ◽  
Markov State ◽  
Markov State Models ◽  
State Models
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